Read in the labelled object Run the correlation comparison between the total scRNA per FACS pop and the AB levels Calculate the proportion of the cell types from the orginal labels UMAP of the cell POPS merged UMAP of the subtypes labelled
library(Seurat)
Attaching SeuratObject
Attaching sp
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library('reshape')
Attaching package: ‘reshape’
The following object is masked from ‘package:dplyr’:
rename
library("ggplot2")
Read in the lableled object single cell sequencing
saveRDS(seu.sc,,paste(pathway, "CombinedLabeledMarkers14102022.RDS"))
Error in saveRDS(seu.sc, , paste(pathway, "CombinedLabeledMarkers14102022.RDS")) :
'file' must be non-empty string
NA
Read in the Flow Cytometry data
output_path = "/Users/rhalenathomas/Documents/Data/FlowCytometry/PhenoID/Analysis/PaperFigures/PostSortGatingPops/"
seu.fc <- readRDS(paste(output_path,"june10postSort.seu.RDS", sep = ""))
unique(seu.fc$GatedPop)
[1] Astrocytes RadialGlia Neurons1 Neurons2
Levels: Astrocytes RadialGlia Neurons1 Neurons2
Marker list
marker.ab <- c("CD24","CD56","CD29","CD15","CD184","CD133","CD71","CD44","GLAST","AQP4","HepaCAM", "CD140a","O4")
marker.genes <-c("CD24","NCAM1","ITGB1","FUT4","CXCR4","PROM1","TFRC","CD44","SLC1A3","AQP4","HEPACAM", "PDGFRA","NKX6-2")
Make the expression matrixes
seu.sc <- ScaleData(object = seu.sc, features = rownames(seu.sc))
Centering and scaling data matrix
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Calculate correlation between two matrixes
df.cor
Astrocytes RadialGlia Neurons1 Neurons2
Astrocytes 0.5553328 0.1522878 -0.4517431 -0.18064326
RadialGlia 0.1305815 0.4451073 -0.3314321 0.05559489
Neurons1 -0.4098105 -0.3016957 0.4534910 0.05175022
Neurons2 -0.4858683 -0.1624758 0.3770386 0.23752196
write.csv(df.cor, paste(output_path,"CorrelationsFCscRNAseq.csv"))
Plot the heatmap Figure 6A
# need to melt the matrix
longData<- melt(df.cor)
Warning in type.convert.default(X[[i]], ...) :
'as.is' should be specified by the caller; using TRUE
Warning in type.convert.default(X[[i]], ...) :
'as.is' should be specified by the caller; using TRUE
head(longData)
colnames(longData) <- c("FC","scRNA","R2")
ggplot(longData, aes(x=scRNA,y=FC, fill =R2)) + geom_tile() +
scale_fill_gradient2(low = "#075AFF",
mid = "#FFFFCC",
high = "#FF0000") +
guides(fill = guide_colourbar(label = TRUE,
ticks = FALSE)) + theme_bw() +
coord_fixed() +scale_x_discrete(expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+ theme(text = element_text(size=16, colour = "black"),
axis.text.x = element_text(size = 18, colour = "black", angle = 90), axis.text.y = element_text(size= 18, colour = "black")) + xlab('scRNAseq') + ylab('Flow Cytometry')
output_path <- "/Users/rhalenathomas/Documents/Projects_Papers/PhenoID/ForFigures/scRNA/"
pdf(paste(output_path,"FC_scRNA_correlationOct16.pdf"), width = 8, height = 5)
ggplot(longData, aes(x=scRNA,y=FC, fill =R2)) + geom_tile() +
scale_fill_gradient2(low = "#075AFF",
mid = "#FFFFCC",
high = "#FF0000", midpoint = 0, limit = c(-0.5,0.5)) +
guides(fill = guide_colourbar(label = TRUE,
ticks = FALSE)) + theme_bw() +
coord_fixed() +scale_x_discrete(expand=c(0,0)) +
scale_y_discrete(expand=c(0,0)) +
theme(text = element_text(size=16, colour = "black"),
axis.text.x = element_text(size = 16, colour = "black", angle = 90), axis.text.y = element_text(size= 16, colour = "black")) + xlab('scRNAseq') + ylab('Flow Cytometry')
dev.off()
quartz_off_screen
2

Compare proportions of cells in each FACS pop - Figure 6B

Make a table of cell types
library(stringr)
library(reshape)
# for original cell types
df.r <- reshape(df, idvar = "Var2", timevar = "Var1", direction = "wide")
dat1 <- df.r
dat1[] <- lapply(dat1[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat1
write.csv(dat1, paste(output_path,"proportionofCelltypesin4pops.csv"))
### for new main cell types
df.r <- reshape(df.3, idvar = "Var2", timevar = "Var1", direction = "wide")
Error in reshape(df.3, idvar = "Var2", timevar = "Var1", direction = "wide") :
object 'df.3' not found
UMAP with original idents to show overlap (or lack there of) Figure 6C
# Figure 6 C
# UMAP with the 4 orig.idents
sample.order <- c("Astrocytes","RadialGlia","Neurons1","Neurons2")
sample.order <- rev(sample.order)
# colour order to match cell type order
clust.colours <- c("royalblue", "indianred2","palegreen2","springgreen4")
Idents(seu.sc) <- 'orig.ident'
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE)
output_path <- "/Users/rhalenathomas/Documents/Projects_Papers/PhenoID/ForFigures/scRNA/"
### Figure 6C
pdf(paste(output_path,"UMAPscRNAseqMerge4Oct16.pdf"),width = 7, height = 4)
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, label.size = 6) +
theme(legend.text = element_text(size=16), axis.title.y = element_text(size=16),
axis.title.x = element_text(size=16), axis.text.y = element_text(size =16),
axis.text.x = element_text(size =16))
dev.off()
quartz_off_screen
2

## Figure S23 panel
cell.order <- c("Astrocytes","Endothelial","Glia","NPC","Neurons","Other","Radial Glia")
cell.order <- rev(cell.order)
# colour order to match cell type order
clust.colours <- c("chocolate1","deepskyblue","steelblue4","red2","mediumpurple3","burlywood3",
"pink2")
Idents(seu.sc) <- 'Cell_Types'
# designated the order of the splits factor
seu.sc$orig.ident <- factor(x = seu.sc$orig.ident, levels = c("Astrocytes","RadialGlia","Neurons1","Neurons2"))
DimPlot(seu.sc, order = cell.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, split.by = 'orig.ident', ncol = 2)
pdf(paste(output_path,"UMAP_merge_splitbyorigidentOct16.pdf"),width = 12, height = 7.5)
DimPlot(seu.sc, order = cell.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, split.by = 'orig.ident', label.size = 6, ncol = 2) +
theme(legend.text = element_text(size=16), axis.title.y = element_text(size=16),
axis.title.x = element_text(size=16), axis.text.y = element_text(size =16),
axis.text.x = element_text(size =16))
dev.off()
quartz_off_screen
2

# also plot one not split to see the whole thing
#pdf(paste(output_path,"UMAP_merge_CellTypesOct16.pdf"),width = 6.7, height = 4.1)
DimPlot(seu.sc, order = cell.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, label.size = 6) +
theme(legend.text = element_text(size=16), axis.title.y = element_text(size=16),
axis.title.x = element_text(size=16), axis.text.y = element_text(size =16),
axis.text.x = element_text(size =16))

#dev.off()
Figure 6D - UMAP with subgroups
pdf(paste(output_path,"UMAP_CellSubtypeMarkersLablesOct16.pdf"),width = 12, height = 5)
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.25, label = TRUE, repel = TRUE)
dev.off()
null device
1
Make a heatmap


Heatmap or Dotplot Save pdf

save the heatmap Change the colours
pdf(paste(output_path,"HeatMapsubtypesOct17.pdf",sep = ""),width = 11, height = 5)
DoHeatmap(seu.sc, features = feature_list, group.by = 'Cell_Subtype_Markers', group.colors = clust.colours, disp.max = 2, disp.min = -1.5,
angle = 90) + scale_fill_gradientn(colors = c("#154c79", "#eeeee4", "#e28743")) +
theme(axis.text.y = element_text(size = 16))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the
existing scale.
dev.off()
null device
1
Subtype markers
Idents(seu.sc) <- 'Cell_Subtypes'
neurons <- subset(seu.sc, idents = c("Neurons1","Neurons2","Neurons3","Neurons4",
"Neurons5","Neurons6","Neurons7"))
Idents(neurons) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_NeuronsOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(neurons) + theme(text = element_text(size=16, colour = "black"))
dev.off()
null device
1
Idents(seu.sc) <- 'Cell_Subtypes'
neurons.da <- subset(seu.sc, idents = c("DANeurons1","DANeurons2","DANeurons3"))
Idents(neurons.da) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_DANeuronsOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(neurons.da)+ theme(text = element_text(size=16, colour = "black"))
dev.off()
null device
1
Idents(seu.sc) <- 'Cell_Subtypes'
astro <- subset(seu.sc, idents = c("Astrocytes1","Astrocytes2","Astrocytes3"))
Idents(astro) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_AstroOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(astro)+ theme(text = element_text(size=16, colour = "black"))
dev.off()
null device
1
Idents(seu.sc) <- 'Cell_Subtypes'
rg <- subset(seu.sc, idents = c("RadialGlia1","RadialGlia2","RadialGlia3","RadialGlia4",
"RadialGlia5","RadialGlia6"))
Idents(rg) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_RGOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(rg)+ theme(text = element_text(size=16, colour = "black"))
dev.off()
null device
1
DotPlots of markers in cell type subsets
n.markers <- c("ASCL1","CP","GRIA2","MGP","SPARCL1","TPH1","TFPI2","CD24")
pdf(paste(output_path,"Dotplot_NeuSubMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(neurons, features = n.markers) +
theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
dev.off()
null device
1
da.markers <- c("RAB3B","TPBG","TTR","TH","SOX6","CALB1","SLC17A6")
pdf(paste(output_path,"Dotplot_DANeuSubMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(neurons.da, features = da.markers) +
theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
Warning: Scaling data with a low number of groups may produce misleading results
dev.off()
null device
1
rg.markers <- c("CYP1B1","NEAT1","PTN","RPL41","TOP2A","VCAN","SOX2","SLIT2","HES1","VIM")
pdf(paste(output_path,"Dotplot_RGMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(rg, features = rg.markers) +
theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
dev.off()
null device
1
# Apoe, Gfap, Aqp4 and Slc1a3
astro.markers <- c("COL3A1","FABP5","HPD","APOE","S100B","IGFBP2","DBI",
"PRSS56", "IGTP","LFIT3","LIGP1",
"COL1A2")
pdf(paste(output_path,"Dotplot_AstroMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(astro, features = astro.markers) +
theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: IGTP, LFIT3, LIGP1
Warning: Scaling data with a low number of groups may produce misleading results
dev.off()
null device
1
Make a label level of main cell types from the merge data
unique(seu.sc$Cell_Subtype_Markers)
[1] Neurons-GRIA2 NPC Neurons-SPARCL1 DANeurons-TPBG Neurons-CP
[6] DANeurons-RAB3B DANeurons-TTR RadialGlia-CY1B1 RadialGlia-RPL41 RadialGlia-PTN
[11] Astrocytes-FABP5 Astrocytes-HPD RadialGlia-TOP2A Mix Neurons-TPH1
[16] Neurons-TFPI1 Neurons-ASCL1 RadialGlia-NEAT1 RadialGlia-VCAN Neurons-MGP
[21] Astrocytes-COL3A1
21 Levels: Astrocytes-HPD Neurons-ASCL1 RadialGlia-RPL41 Neurons-SPARCL1 ... RadialGlia-VCAN
Idents(seu.sc) <- 'Cell_Subtype_Markers'
cluster.ids <- c("Astrocytes","Neurons","RadialGlia","Neurons",
"Astrocytes","Neurons","RadialGlia","Neurons",
"Astrocytes","Neurons","RadialGlia",
"DANeurons","Neurons","Mix","NPC",
"Neurons","DANeurons","DANeurons",
"RadialGlia","RadialGlia","RadialGlia"
)
names(cluster.ids) <- levels(seu.sc)
seu.sc <- RenameIdents(seu.sc, cluster.ids)
seu.sc$Cell_Type2 <- Idents(seu.sc)
Idents(seu.sc) <- 'Cell_Type2'
DimPlot(seu.sc)

DimPlot(seu.sc, group.by = 'Cell_Subtype_Markers')

DimPlot(seu.sc, group.by = 'Cell_Subtypes')

DimPlot(seu.sc, group.by = 'Cell_Type2')

NA
NA
# original clusters proportion of cell types in
library(reshape2)
pr.celltypes <- as.data.frame(table(seu.sc$orig.ident,seu.sc$Cell_Type2))
pr.celltypes <- reshape(pr.celltypes, idvar = "Var2", timevar = "Var1", direction = "wide")
pr.celltypes
table(seu.sc$Cell_Type2)
Astrocytes Neurons RadialGlia DANeurons Mix NPC
3082 2870 1913 476 228 154
write.csv(pr.celltypes, paste(output_path,"FreqCellTypes2inFACS.csv"))
Proportion of cell types in original FACS populations
pr
1 22 43 64
Var2 "Neurons2" "Neurons1" "Astrocytes" "RadialGlia"
Freq.DANeurons-RAB3B "131" " 76" " 0" " 52"
Freq.DANeurons-TPBG "10" "50" " 7" "46"
Freq.DANeurons-TTR "65" "20" " 0" "19"
Proportion of cell types tests
library("scProportionTest")
prop_test <- sc_utils(seu.sc)
prop_test <- permutation_test(
prop_test, cluster_identity = "Cell_Type2",
sample_1 = "Neurons1", sample_2 = "Neurons2",
sample_identity = "orig.ident"
)
permutation_plot(prop_test)

prop_test <- permutation_test(
prop_test, cluster_identity = "Cell_Type2",
sample_1 = "Neurons1", sample_2 = "RadialGlia",
sample_identity = "orig.ident"
)
permutation_plot(prop_test)

prop_test <- permutation_test(
prop_test, cluster_identity = "Cell_Type2",
sample_1 = "Neurons2", sample_2 = "RadialGlia",
sample_identity = "orig.ident"
)
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
permutation_plot(prop_test)

prop_test <- permutation_test(
prop_test, cluster_identity = "Cell_Type2",
sample_1 = "Astrocytes", sample_2 = "RadialGlia",
sample_identity = "orig.ident"
)
permutation_plot(prop_test)

Check the proportions of subtypes of DA neurons
#library("scProportionTest")
prop_test <- sc_utils(neurons.da)
prop_test <- permutation_test(
prop_test, cluster_identity = "Cell_Subtype_Markers",
sample_1 = "Neurons1", sample_2 = "Neurons2",
sample_identity = "orig.ident"
)
permutation_plot(prop_test)

Differential gene expression between Neurons1 and Neurons2 for Other neurons and DA neurons.
down <- rownames(deg.neurons %>% filter(avg_log2FC < -6))
length(down)
[1] 8
In DA neurons
DotPlot(sub.neur, group.by = 'orig.ident', features = up.down) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results

Look at the GO and other pathway analysis for the DGE
For DA neurons
pdf(paste(output_path,"GOresultesNeurons1vs2.pdf"), width = 12, height = 6)
plotEnrich(t.GObio.da1, showTerms = 10, numChar = 40, y = "Count", orderBy = "Combined.Score") +
theme(text = element_text(size=16, colour = "black"))
plotEnrich(t.GObio.da1, showTerms = 10, numChar = 40, y = "Count", orderBy = "Overlap")+
theme(text = element_text(size=16, colour = "black"))
plotEnrich(t.GObio.da2, showTerms = 10, numChar = 40, y = "Count", orderBy = "Combined.Score")+
theme(text = element_text(size=16, colour = "black"))
plotEnrich(t.GObio.da2, showTerms = 10, numChar = 40, y = "Count", orderBy = "Overlap")+
theme(text = element_text(size=16, colour = "black"))
dev.off()
null device
1
Dot plots of some up regulated genes
DotPlot(all.neurons, group.by = 'orig.ident', features = regulate.genes) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
pdf(paste(output_path,"DotPlotDEG_N1vsN2.pdf"))
DotPlot(all.neurons, group.by = 'orig.ident', features = regulate.genes) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
dev.off()
quartz_off_screen
2

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library(clusterProfiler)
clusterProfiler v4.4.4 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
Attaching package: ‘clusterProfiler’
The following object is masked from ‘package:reshape’:
rename
The following object is masked from ‘package:stats’:
filter
library(enrichplot)
# we use ggplot2 to add x axis labels (ex: ridgeplot)
library(ggplot2)
Try with cluster profiler
emapplot(gse, showCategory = 10)
Error in has_pairsim(x) :
Term similarity matrix not available. Please use pairwise_termsim function to deal with the results of enrichment analysis.
Predict cell types again to make table 12 Organoids in house data
seu.r <- AIW120
colnames(seu.r@meta.data)
[1] "orig.ident" "nCount_RNA"
[3] "nFeature_RNA" "percent.mt"
[5] "predicted.id" "prediction.score.RGa"
[7] "prediction.score.Astrocytes.2" "prediction.score.Neurons.e"
[9] "prediction.score.Oligodendrocytes" "prediction.score.Neurons.DA"
[11] "prediction.score.Neural.Precursors" "prediction.score.Neurons.i"
[13] "prediction.score.Neurons" "prediction.score.Other"
[15] "prediction.score.Epithelial" "prediction.score.Astrocytes.1"
[17] "prediction.score.RGd1" "prediction.score.RGd2"
[19] "prediction.score.max" "pred.165days"
[21] "RNA_snn_res.0" "RNA_snn_res.0.05"
[23] "RNA_snn_res.0.25" "RNA_snn_res.0.5"
[25] "RNA_snn_res.0.8" "seurat_clusters"
[27] "RNA_snn_res.0.9" "RNA_snn_res.1"
[29] "RNA_snn_res.1.2" "res08names"
[31] "res08names2" "res08names.gene2"
[33] "main.genotype" "res08names.gene3"
[35] "res08names3"
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 4595 anchors
Filtering anchors
Retained 2761 anchors
as(<ngCMatrix>, "dgCMatrix") is deprecated since Matrix 1.5-0; do as(., "dMatrix") instead
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = seu.r$res08names2, k.weight = 50)
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
seu.q$AIW120.pred <- ifelse(seu.q$prediction.score.max > 0.8, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'AIW120.pred')

More predictions Developing brain: Cortex Forebrain
Adult brain: whole brain with subtypes Adult brain midbrain and striatum Adult brain midbrain main cell types Adult brain DA or Astro subtypes

Tables of top predictions
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$devcortex))
t.lables$Freq <- as.double(t.lables$Freq)
top.prediction <-as.data.frame(t.lables %>% group_by(Var1) %>% top_n(1, Freq))
top.prediction
---
title: "R Notebook"
output: html_notebook
---

Read in the labelled object 
Run the correlation comparison between the total scRNA per FACS pop and the AB levels
Calculate the proportion of the cell types from the orginal labels
UMAP of the cell POPS merged
UMAP of the subtypes labelled




```{r}

library(Seurat)
library(dplyr)
library('reshape')
library("ggplot2")


```


Read in the lableled object single cell sequencing

```{r}

pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"
seu.sc <- readRDS(paste(pathway, "CombinedLabeledMarkers14102022.RDS"))
saveRDS(seu.sc,paste(pathway, "CombinedLabeledMarkers14102022.RDS"))

```


Read in the Flow Cytometry data

```{r}
output_path = "/Users/rhalenathomas/Documents/Data/FlowCytometry/PhenoID/Analysis/PaperFigures/PostSortGatingPops/"
seu.fc <- readRDS(paste(output_path,"june10postSort.seu.RDS", sep = ""))
unique(seu.fc$GatedPop)

# both neurons1 and neurons2 are mixed ie what I labelled before sorting as neurons1 (CD24+++) was labelled as Neurons2 in the expeiment

```

Marker list

```{r}

marker.ab <- c("CD24","CD56","CD29","CD15","CD184","CD133","CD71","CD44","GLAST","AQP4","HepaCAM", "CD140a","O4")

marker.genes <-c("CD24","NCAM1","ITGB1","FUT4","CXCR4","PROM1","TFRC","CD44","SLC1A3","AQP4","HEPACAM", "PDGFRA","NKX6-2")

```


Make the expression matrixes

```{r}
Idents(seu.sc) <- 'orig.ident'
DefaultAssay(seu.sc) <- 'RNA'

# to include all genes in scale data add the features argument
seu.sc <- ScaleData(object = seu.sc, features = rownames(seu.sc))


scRNAseq.mean <- as.data.frame(AverageExpression(seu.sc,features = marker.genes, assays = 'RNA',
                                   group.by = 'orig.ident', slot= 'scale.data'))


# change gene names to protein/AB names
rownames(scRNAseq.mean) <- marker.ab

# get the mean expression from FACS 
DefaultAssay(seu.fc) <- 'RNA'
Idents(seu.fc) <- 'GatedPop'

#seu.fc <- ScaleData(seu.fc)

FC.mean <- as.data.frame(AverageExpression(seu.fc, assays = 'RNA',features = marker.ab,
                                   group.by = 'GatedPop', slot= 'scale.data'))


```

Calculate correlation between two matrixes

```{r}

# rename and reorder dataframes to match
colnames(FC.mean)
colnames(FC.mean) <- c("Astrocytes","RadialGlia","Neurons2","Neurons1")
colnames(FC.mean)

colnames(scRNAseq.mean)
colnames(scRNAseq.mean) <- c("Neurons2","Neurons1","Astrocytes","RadialGlia")
colnames(scRNAseq.mean)
rna.mean <- scRNAseq.mean %>% select("Astrocytes","RadialGlia","Neurons1","Neurons2")
FC.mean <- FC.mean %>% select("Astrocytes","RadialGlia","Neurons1","Neurons2")

df.cor <- cor(FC.mean, rna.mean)
# first one is the rows and second is the columns, FC measures on the y axis

df.cor


# Neurons2 should be the high CD56 population and it is in both cases 
# Neurons1 should be the high CD24 and it is

FC.mean
rna.mean

# try calculating z scores
# this works on rows
data <- FC.mean
add.rownames <- rownames(data)
FC.mean.z <- as.data.frame(sapply(data, function(data) (data-mean(data))/sd(data)))

rownames(FC.mean.z) <-add.rownames
FC.mean.z


data <- rna.mean
add.rownames <- rownames(data)
rna.mean.z <- as.data.frame(sapply(data, function(data) (data-mean(data))/sd(data)))

rownames(rna.mean.z) <-add.rownames
rna.mean.z


df.cor <- cor(FC.mean.z, rna.mean.z)
df.cor

write.csv(df.cor, paste(output_path,"CorrelationsFCscRNAseq.csv"))


```

Plot the heatmap
Figure 6A

```{r}
# need to melt the matrix
longData<- melt(df.cor)

head(longData)
colnames(longData) <- c("FC","scRNA","R2")

ggplot(longData, aes(x=scRNA,y=FC, fill =R2)) + geom_tile() +
  scale_fill_gradient2(low = "#075AFF",
                       mid = "#FFFFCC",
                       high = "#FF0000") +
   guides(fill = guide_colourbar(label = TRUE,
                                ticks = FALSE)) + theme_bw() +
  coord_fixed()  +scale_x_discrete(expand=c(0,0))+
  scale_y_discrete(expand=c(0,0))+ theme(text = element_text(size=16, colour = "black"),
        axis.text.x = element_text(size = 18, colour = "black", angle = 90), axis.text.y = element_text(size= 18, colour = "black"))  + xlab('scRNAseq') + ylab('Flow Cytometry') 

output_path <- "/Users/rhalenathomas/Documents/Projects_Papers/PhenoID/ForFigures/scRNA/"

pdf(paste(output_path,"FC_scRNA_correlationOct16.pdf"), width = 8, height = 5)
ggplot(longData, aes(x=scRNA,y=FC, fill =R2)) + geom_tile() +
  scale_fill_gradient2(low = "#075AFF",
                       mid = "#FFFFCC",
                       high = "#FF0000", midpoint = 0, limit = c(-0.5,0.5)) +
   guides(fill = guide_colourbar(label = TRUE,
                                ticks = FALSE)) + theme_bw() +
  coord_fixed()  +scale_x_discrete(expand=c(0,0)) +
  scale_y_discrete(expand=c(0,0)) + 
  theme(text = element_text(size=16, colour = "black"),
        axis.text.x = element_text(size = 16, colour = "black", angle = 90), axis.text.y = element_text(size= 16, colour = "black"))  + xlab('scRNAseq') + ylab('Flow Cytometry') 

dev.off()




```

Compare proportions of cells in each FACS pop - Figure 6B

```{r}

# save the proportion chart with colours matching the UMAPs 
df <- as.data.frame(table(seu.sc$orig.ident, seu.sc$Cell_Types))

# the chart will default to alphabetical

clust.colours <- c("chocolate1","deepskyblue","steelblue4","mediumpurple3","red2","burlywood3",     "pink2")

# but not for the x axis - reorder with factor to match figure 6A
df$FACpop <- factor(df$Var1, levels = c("Astrocytes","RadialGlia","Neurons1","Neurons2"))

pdf(paste(output_path,"BarChartProportionCellTypesin4popsOct16.pdf"), height = 5, width = 6)
ggplot(df, aes(x = FACpop, y = Freq, fill = Var2)) + 
  geom_col(position = "fill") + theme_classic() +
  scale_fill_manual(values = clust.colours) +
  #scale_x_discrete(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0,0)) +
  theme(axis.text.x = element_text(angle = 90, size = 16), axis.text.y = element_text(size = 16)) +
  labs(y = "Proportion of Cells", x = "") +
  theme(axis.title.y = element_text(size = 16), legend.text = element_text(size = 16), legend.title = element_text(size = 16)) 
dev.off()

```


Make a table of cell types

```{r}

library(stringr)
library(reshape)

# for original cell types 


df.r <- reshape(df, idvar = "Var2", timevar = "Var1", direction = "wide")
dat1 <- df.r
dat1[] <- lapply(dat1[], function(x){
  # Check if the column is numeric
  if (is.numeric(x)){
    return(x/sum(x)*100)
  } else{
    return(x)
  }
})
dat1
write.csv(dat1, paste(output_path,"proportionofCelltypesin4pops.csv"))




```



UMAP with original idents to show overlap (or lack there of)
Figure 6C

```{r}
# Figure 6 C
# UMAP with the 4 orig.idents

sample.order <- c("Astrocytes","RadialGlia","Neurons1","Neurons2")
sample.order <- rev(sample.order)

# colour order to match cell type order
clust.colours <- c("royalblue", "indianred2","palegreen2","springgreen4")
 
Idents(seu.sc) <- 'orig.ident'

RunUMAP()
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE)

output_path <- "/Users/rhalenathomas/Documents/Projects_Papers/PhenoID/ForFigures/scRNA/"

### Figure 6C

pdf(paste(output_path,"UMAPscRNAseqMerge4Oct16.pdf"),width = 7, height = 4)
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, label.size = 6) +
  theme(legend.text = element_text(size=16), axis.title.y = element_text(size=16), 
        axis.title.x = element_text(size=16), axis.text.y = element_text(size =16),
        axis.text.x = element_text(size =16))
dev.off()


## Figure S23 panel 

cell.order <- c("Astrocytes","Endothelial","Glia","NPC","Neurons","Other","Radial Glia")
cell.order <- rev(cell.order)

# colour order to match cell type order
clust.colours <- c("chocolate1","deepskyblue","steelblue4","red2","mediumpurple3","burlywood3", 
                   "pink2")

 
Idents(seu.sc) <- 'Cell_Types'
      
# designated the order of the splits factor
seu.sc$orig.ident <- factor(x = seu.sc$orig.ident, levels = c("Astrocytes","RadialGlia","Neurons1","Neurons2"))


DimPlot(seu.sc, order = cell.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, split.by = 'orig.ident', ncol = 2)




pdf(paste(output_path,"UMAP_merge_splitbyorigidentOct16.pdf"),width = 12, height = 7.5)
DimPlot(seu.sc, order = cell.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, split.by = 'orig.ident', label.size = 6, ncol = 2) +
  theme(legend.text = element_text(size=16), axis.title.y = element_text(size=16), 
        axis.title.x = element_text(size=16), axis.text.y = element_text(size =16),
        axis.text.x = element_text(size =16))
dev.off()


# also plot one not split to see the whole thing


pdf(paste(output_path,"UMAP_merge_CellTypesOct16.pdf"),width = 6.7, height = 4.1)
DimPlot(seu.sc, order = cell.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.1, label = FALSE, label.size = 6) +
  theme(legend.text = element_text(size=16), axis.title.y = element_text(size=16), 
        axis.title.x = element_text(size=16), axis.text.y = element_text(size =16),
        axis.text.x = element_text(size =16))
dev.off()





```

Figure 6D - UMAP with subgroups

```{r}

unique(seu.sc$Cell_Subtype_Markers)
# 21 levels will need 21 colours 
# Neurons - purples
# DA neurons green
# NPC red
# Astrocytes - Orange -
# Radial Glia - Pink yellow
# other cells blues

sample.order <- c("Astrocytes-COL3A1","Astrocytes-FABP5", "Astrocytes-HPD",
                  "DANeurons-RAB3B","DANeurons-TPBG","DANeurons-TTR",
                  "Mix","NPC",
                  "Neurons-ASCL1",
                  "Neurons-CP","Neurons-GRIA2",
                  "Neurons-MGP",
                  "Neurons-SPARCL1",
                  "Neurons-TPH1", "Neurons-TFPI1",
                  "RadialGlia-CY1B1", "RadialGlia-NEAT1","RadialGlia-PTN",
                  "RadialGlia-RPL41", "RadialGlia-TOP2A", "RadialGlia-VCAN")
sample.order <- rev(sample.order)


clust.colours <- c("#F1C166","#FF9400","#E95901",          # oranges Astrocytes
                   "#43D59C","#7DA696","#15AE36",     # greens DA neurons
                   "steelblue","red2",   # mix and NPC
                   "#8F67FF",
                   "#6840DB","#9B8BC7",   # neurons 2  CP, GRIA2
                   "#7B22FB",    # MGP
                   "purple", # SPARCL1
                   "#9863E5","#8E36D2", # TPH1, TFPI1
                   "#F68D8D", # RG CY1B1
                   "#F68888","#F688BD",   # RG NEAT1, PTN 
                   "#F5288A","#DC57CF","#F3A6EC")

#DimPlot(seu.sc, pt.size = 0.1, order = sample.order, label = TRUE, group.by = 'Cell_Subtype_Markers')
Idents(seu.sc) <- 'Cell_Subtype_Markers' 

DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.25, label = TRUE)

DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.25, label = FALSE)



#Idents(seu.sc) <- 'Cell_Subtypes'
#DimPlot(seu.sc, shuffle = TRUE, raster=FALSE, pt.size = 0.25, label = TRUE)


# save Figure 6D
#pdf(paste(output_path,"UMAP_CellSubtypeMarkersOct16.pdf"),width = 9.4, height = 3.9)
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.25, label = FALSE)
#dev.off()


# Figure S25
# with labels
pdf(paste(output_path,"UMAP_CellSubtypeMarkersLablesOct16.pdf"),width = 12, height = 5)
DimPlot(seu.sc, order = sample.order, cols = clust.colours, shuffle = TRUE, raster=FALSE, pt.size = 0.25, label = TRUE, repel = TRUE)
dev.off()



```


Make a heatmap

```{r}

# some DA classic marker
# some neuronal markers
# some 
PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

#likely too many
ft <- c("COL3A1","FABP5","HPD","RAB3B","TPBG","TTR","SOX2","ASCL1","CP","GRIA2",
        "MGP","SPARCL1","TPH1","TFPI1","CY1B1","NEAT1","PTN","RPL41","TOP2A","VCAN")

# to change the cell type order
Idents(seu.sc) <- 'Cell_Subtype_Markers'



seu.sc$Cell_Subtype_Markers <- factor(seu.sc$Cell_Subtype_Markers, levels = sample.order)

#seu.sc <- ScaleData(seu.sc)
#DoHeatmap(seu.sc, features = PD_poulin, group.by = 'Cell_Subtype_Markers', slot = 'scale.data')
DotPlot(seu.sc, features = ft, group.by = 'Cell_Subtype_Markers') +
  theme(axis.text.x = element_text(angle = 90))

# these are not very helpful markers 
# need to reverse sample order for heatmaps
DoHeatmap(seu.sc, features = ft, group.by = 'Cell_Subtype_Markers')

ft = c()

feature_list = c("PAX6","OTX2","VIM","SLC1A3","SOX2","HES1","NES","S100B","SOX9","MAP2",
                 "NCAM1","CD24","GRIA2","GABBR1")

feature_list = c("TH","PAX6","OTX2","MAP2","NCAM1","CD24","GRIA2","GABBR1",
                 "VIM","SLC1A3","SOX2","HES1","NES","S100B","SOX9","GFAP"
                 )

"GRIA2","GABBR1"

feature_list = c("MAP2","NCAM1","CD24","GRIA2")

feature_list = c("MAP2","NCAM1","CD24","GRIA2","GRIN2B",,"GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VCAM1")

"SOX9"
feature_list <- c("RBFOX3","GRIN2B","GAD1","GAD2","GABRA1","GABRB2","TH")

feature_list <- c("SLC1A3", "PAX6", "SOX2", "PDGFD", "GLI3", "STMN2", "NEUROD6", "VIM", "HES1")

DotPlot(seu.sc, features = feature_list, group.by = 'Cell_Subtype_Markers') +
  theme(axis.text.x = element_text(angle = 90))

# these are not very helpful markers 
# need to reverse sample order for heatmaps
DoHeatmap(seu.sc, features = feature_list, group.by = 'Cell_Subtype_Markers')




```

Heatmap or Dotplot 
Save pdf


```{r}


feature_list = c("TH","PAX6","OTX2","MAP2","NCAM1","CD24","GRIA2","GABBR1",
                 "VIM","SLC1A3","SOX2","HES1","NES","S100B","SOX9"
                 )

feature_list.d = c("PAX6","VIM","SLC1A3","SOX2","HES1","NES","OTX2",
                 "MAP2","NCAM1","CD24","GRIA2","GABBR1","S100B","SOX9"
                 )


DotPlot(seu.sc, features = feature_list.d, group.by = 'Cell_Subtype_Markers') +
  theme(axis.text.x = element_text(angle = 90))

DotPlot(seu.sc, features = "TH", group.by = 'Cell_Subtype_Markers') +
  theme(axis.text.x = element_text(angle = 90))
# these are not very helpful markers 
# need to reverse sample order for heatmaps

sample.order.r <- rev(sample.order)

seu.sc$Cell_Subtype_Markers <- factor(seu.sc$Cell_Subtype_Markers, levels = sample.order.r)


# save the dotplots
pdf(paste(output_path,"DotPlotCellSubtypesOct17.pdf"))
DotPlot(seu.sc, features = feature_list.d, group.by = 'Cell_Subtype_Markers') +
  theme(axis.text.x = element_text(angle = 90))
dev.off()

pdf(paste(output_path,"DotPlotTHbysubtypesOct17.pdf"))
DotPlot(seu.sc, features = "TH", group.by = 'Cell_Subtype_Markers') +
  theme(axis.text.x = element_text(angle = 90))
dev.off()

```

save the heatmap
Change the colours

```{r}

feature_list = c("TH","PAX6","OTX2","MAP2","NCAM1","CD24","GRIA2","GABBR1",
                 "VIM","SLC1A3","SOX2","HES1","NES","S100B","SOX9"
                 )

# reverse the cell type order
sample.order.r <- rev(sample.order)

seu.sc$Cell_Subtype_Markers <- factor(seu.sc$Cell_Subtype_Markers, levels = sample.order.r)

pdf(paste(output_path,"HeatMapsubtypesOct17.pdf",sep = ""),width = 11, height = 5)
DoHeatmap(seu.sc, features = feature_list, group.by = 'Cell_Subtype_Markers', group.colors = clust.colours, disp.max = 2, disp.min = -1.5,
          angle = 90) + scale_fill_gradientn(colors = c("#154c79", "#eeeee4", "#e28743")) + 
  theme(axis.text.y = element_text(size = 16))
dev.off()


```


Subtype markers


```{r}
Idents(seu.sc) <- 'Cell_Subtypes'
neurons <- subset(seu.sc, idents = c("Neurons1","Neurons2","Neurons3","Neurons4",
                                     "Neurons5","Neurons6","Neurons7"))

Idents(neurons) <- 'Cell_Subtype_Markers'

pdf(paste(output_path,"UMAP_NeuronsOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(neurons) +  theme(text = element_text(size=16, colour = "black"))
dev.off()

Idents(seu.sc) <- 'Cell_Subtypes'
neurons.da <- subset(seu.sc, idents = c("DANeurons1","DANeurons2","DANeurons3"))

Idents(neurons.da) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_DANeuronsOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(neurons.da)+  theme(text = element_text(size=16, colour = "black"))
dev.off()

Idents(seu.sc) <- 'Cell_Subtypes'
astro <- subset(seu.sc, idents = c("Astrocytes1","Astrocytes2","Astrocytes3"))
Idents(astro) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_AstroOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(astro)+  theme(text = element_text(size=16, colour = "black"))
dev.off()

Idents(seu.sc) <- 'Cell_Subtypes'
rg <- subset(seu.sc, idents = c("RadialGlia1","RadialGlia2","RadialGlia3","RadialGlia4",
                                "RadialGlia5","RadialGlia6"))
Idents(rg) <- 'Cell_Subtype_Markers'
pdf(paste(output_path,"UMAP_RGOct17.pdf",sep = ""),width = 7, height = 4)
DimPlot(rg)+  theme(text = element_text(size=16, colour = "black"))
dev.off()




```


DotPlots of markers in cell type subsets

```{r}

n.markers <- c("ASCL1","CP","GRIA2","MGP","SPARCL1","TPH1","TFPI2","CD24")

pdf(paste(output_path,"Dotplot_NeuSubMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(neurons, features = n.markers) + 
  theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
dev.off()

da.markers <- c("RAB3B","TPBG","TTR","TH","SOX6","CALB1","SLC17A6")

pdf(paste(output_path,"Dotplot_DANeuSubMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(neurons.da, features = da.markers) + 
  theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
dev.off()

rg.markers <- c("CYP1B1","NEAT1","PTN","RPL41","TOP2A","VCAN","SOX2","SLIT2","HES1","VIM")

pdf(paste(output_path,"Dotplot_RGMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(rg, features = rg.markers) + 
  theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
dev.off()

# Apoe, Gfap, Aqp4 and Slc1a3
astro.markers <- c("COL3A1","FABP5","HPD","APOE","S100B","IGFBP2","DBI",
                   "PRSS56", "IGTP","LFIT3","LIGP1",
                   "COL1A2")

pdf(paste(output_path,"Dotplot_AstroMarkersOct17.pdf",sep = ""),width = 7, height = 4)
DotPlot(astro, features = astro.markers) + 
  theme(text = element_text(size=16, colour = "black"),axis.text.x = element_text(angle = 90))
dev.off()


```

Make a label level of main cell types from the merge data

```{r}

unique(seu.sc$Cell_Subtype_Markers)
Idents(seu.sc) <- 'Cell_Subtype_Markers'

cluster.ids <- c("Astrocytes","Neurons","RadialGlia","Neurons",
                 "Astrocytes","Neurons","RadialGlia","Neurons",
                 "Astrocytes","Neurons","RadialGlia",
                 "DANeurons","Neurons","Mix","NPC",
                 "Neurons","DANeurons","DANeurons",
                 "RadialGlia","RadialGlia","RadialGlia"
                 )


names(cluster.ids) <- levels(seu.sc)
seu.sc <- RenameIdents(seu.sc, cluster.ids)
seu.sc$Cell_Type2 <- Idents(seu.sc)
Idents(seu.sc) <- 'Cell_Type2'
DimPlot(seu.sc)

DimPlot(seu.sc, group.by = 'Cell_Subtype_Markers')
DimPlot(seu.sc, group.by = 'Cell_Subtypes')
DimPlot(seu.sc, group.by = 'Cell_Type2')


```


```{r}

# original clusters proportion of cell types in 
library(reshape2)

pr.celltypes <- as.data.frame(table(seu.sc$orig.ident,seu.sc$Cell_Type2))
pr.celltypes <- reshape(pr.celltypes, idvar = "Var2", timevar = "Var1", direction = "wide")
pr.celltypes

table(seu.sc$Cell_Type2)

write.csv(pr.celltypes, paste(output_path,"FreqCellTypes2inFACS.csv"))

```



Proportion of cell types in original FACS populations


```{r}

# original clusters proportion of cell types in 
library(reshape2)

pr.celltypes <- as.data.frame(table(neurons.da$Cell_Subtype_Markers,neurons.da$orig.ident))
pr.celltypes <- reshape(pr.celltypes, idvar = "Var2", timevar = "Var1", direction = "wide")
pr.celltypes
pr <- pr.celltypes %>% select(Var2,`Freq.DANeurons-RAB3B`,`Freq.DANeurons-TPBG`,`Freq.DANeurons-TTR`)
pr
pr <- t(pr)
pr
write.csv(pr, paste(output_path,"CellCountsDAneuronsPerFACSpop.csv"))


```


Proportion of cell types tests

```{r}

library("scProportionTest")

prop_test <- sc_utils(seu.sc)

prop_test <- permutation_test(
	prop_test, cluster_identity = "Cell_Type2",
	sample_1 = "Neurons1", sample_2 = "Neurons2",
	sample_identity = "orig.ident"
)


permutation_plot(prop_test)

prop_test <- permutation_test(
	prop_test, cluster_identity = "Cell_Type2",
	sample_1 = "Neurons1", sample_2 = "RadialGlia",
	sample_identity = "orig.ident"
)
permutation_plot(prop_test)


prop_test <- permutation_test(
	prop_test, cluster_identity = "Cell_Type2",
	sample_1 = "Neurons2", sample_2 = "RadialGlia",
	sample_identity = "orig.ident"
)
permutation_plot(prop_test)


prop_test <- permutation_test(
	prop_test, cluster_identity = "Cell_Type2",
	sample_1 = "Astrocytes", sample_2 = "RadialGlia",
	sample_identity = "orig.ident"
)
permutation_plot(prop_test)

```


Check the proportions of subtypes of DA neurons

```{r}

#library("scProportionTest")

prop_test <- sc_utils(neurons.da)

prop_test <- permutation_test(
	prop_test, cluster_identity = "Cell_Subtype_Markers",
	sample_1 = "Neurons1", sample_2 = "Neurons2",
	sample_identity = "orig.ident"
)


permutation_plot(prop_test)

```


Differential gene expression between Neurons1 and Neurons2 for Other neurons and DA neurons. 

```{r}
Idents(seu.sc) <- 'Cell_Type2'
sub.neur <- subset(seu.sc, idents = "Neurons")
DimPlot(sub.neur, group.by = 'Cell_Subtype_Markers')

Idents(sub.neur) <- 'orig.ident'
deg.neurons <- FindMarkers(sub.neur, ident.1 = "Neurons1", ident.2 = "Neurons2")

up <- rownames(deg.neurons %>% filter(avg_log2FC > 1.2))
length(up)

down <- rownames(deg.neurons %>% filter(avg_log2FC < -6))
length(down)

up.down <- c(up,down)

DoHeatmap(sub.neur, group.by = 'orig.ident', features = up.down)

up
down


# up is in neurons1 and down is in neurons2

write.csv(deg.neurons, paste(output_path, "DEG.neurons1vsneurons2inNeurons.csv"))


```



In DA neurons

```{r}

Idents(seu.sc) <- 'Cell_Type2'
sub.neur <- subset(seu.sc, idents = "DANeurons")
DimPlot(sub.neur, group.by = 'Cell_Subtype_Markers')

Idents(sub.neur) <- 'orig.ident'
deg.neurons.da <- FindMarkers(sub.neur, ident.1 = "Neurons1", ident.2 = "Neurons2")

up <- rownames(deg.neurons %>% filter(avg_log2FC > 1.2))
length(up)

down <- rownames(deg.neurons %>% filter(avg_log2FC < -0.5))
length(down)
down
down <- down[17:36]
up.down <- c(up,down)

DoHeatmap(sub.neur, group.by = 'orig.ident', features = up.down)

DotPlot(sub.neur, group.by = 'orig.ident', features = up.down) + RotatedAxis()

write.csv(deg.neurons.da, paste(output_path, "DEG.neurons1vsneurons2inDANeurons.csv"))


```


Look at the GO and other pathway analysis for the DGE

```{r}

# for neurons

library(enrichR)

setEnrichrSite("Enrichr") # Human genes
# list of all the databases

# libaries with cell types
#dbs <- listEnrichrDbs()
#dbs
db <- c('GO_Cellular_Component_2018','GO_Biological_Process_2018',
        'GO_Molecular_Function_2018')

Neurons1.up <- deg.neurons %>% filter(p_val_adj < 0.05 & avg_log2FC > 0)
genes <- rownames(Neurons1.up)

Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))

library(dplyr)
t.GOcell.n <- Er[[1]] 
t.GObio.n <- Er[[2]] 
t.GOmol.n <- Er[[3]] 
# synpathetic neurvous sytem dev
# 	FZD3;CTNNB1;SOX11;ASCL1;SOX4
#negative regulation of neuron differentiation (GO:0045665)
# 	EFNB2;ID2;ID1;ID3;HES1;SOX9;CALR;ASCL1

# negative regulation of neurogenesis (GO:0050768)
# ID2;ID1;ID3;PAX6;CALR;ASCL1

# neuronal differentiation
# 	FZD3;ID2;ID1;ID3;CTNNB1;SOX11;OTX2;PAX6;ASCL1;SOX4

# 	ID2;ID1;ID3;SOX11;TCF4;CALR;ASCL1


Neurons2.up <- deg.neurons %>% filter(p_val_adj < 0.05 & avg_log2FC < 0)
genes <- rownames(Neurons2.up)

Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))


t.GOcell.d <- Er[[1]] #%>% select(Term, Genes, Combined.Score)
t.GObio.d <- Er[[2]] #%>% select(Term, Genes, Combined.Score)
t.GOmol.d <- Er[[3]] #%>% select(Term, Genes, Combined.Score)
 

# go biological terms are the most interesting 
  
  
  # neurons2 up 
  #
  
  
  



```

For DA neurons 

```{r}

setEnrichrSite("Enrichr") # Human genes
# list of all the databases

# libaries with cell types
#dbs <- listEnrichrDbs()
#dbs
db <- c('GO_Cellular_Component_2018','GO_Biological_Process_2018',
        'GO_Molecular_Function_2018')

Neurons1.up <- deg.neurons %>% filter(p_val_adj < 0.05 & avg_log2FC > 0)
genes <- rownames(Neurons1.up)

Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "Odds.Ratio"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "Ajusted.P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "Combined.Score"))


t.GObio.da1 <- Er[[2]] 

plotEnrich(t.GObio.da1, showTerms = 10, numChar = 40, y = "Count", orderBy = "Combined.Score")
plotEnrich(t.GObio.da1, showTerms = 10, numChar = 40, y = "Count", orderBy = "Overlap")

# 	EFNB2;ID2;ID1;ID3;HES1;SOX9;CALR;ASCL1
# neuronal differentiation
# 	FZD3;ID2;ID1;ID3;CTNNB1;SOX11;OTX2;PAX6;ASCL1;SOX4
Neurons2.up <- deg.neurons %>% filter(p_val_adj < 0.05 & avg_log2FC < 0)
genes <- rownames(Neurons2.up)

Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "Odds.Ratio"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "Combined.Score"))
t.GObio.da2 <- Er[[2]] 

# many MT gene, also "FTH1","FTL","APOE","SAT1","PTN","GABARAP","PTGDS"

pdf(paste(output_path,"GOresultesNeurons1vs2.pdf"), width = 12, height = 6)
plotEnrich(t.GObio.da1, showTerms = 10, numChar = 40, y = "Count", orderBy = "Combined.Score") +
  theme(text = element_text(size=16, colour = "black"))
plotEnrich(t.GObio.da1, showTerms = 10, numChar = 40, y = "Count", orderBy = "Overlap")+
  theme(text = element_text(size=16, colour = "black"))
plotEnrich(t.GObio.da2, showTerms = 10, numChar = 40, y = "Count", orderBy = "Combined.Score")+
  theme(text = element_text(size=16, colour = "black"))
plotEnrich(t.GObio.da2, showTerms = 10, numChar = 40, y = "Count", orderBy = "Overlap")+
  theme(text = element_text(size=16, colour = "black"))
dev.off()


```


Dot plots of some up regulated genes

```{r}

Idents(seu.sc) <- 'Cell_Type2'
all.neurons <- subset(seu.sc,idents = c("DANeurons","Neurons") )

Idents(all.neurons) <- 'orig.ident'
all.neurons <- subset(all.neurons,idents = c("Neurons1","Neurons2") )

# 	FZD3;ID2;ID1;ID3;CTNNB1;SOX11;OTX2;PAX6;ASCL1;SOX4

regulate.genes <- c("FZD3","CTNNB1","SOX11","ASCL1","SOX4","ID2","ID1","ID3",
                    "TCF4","CALR","OTX2","PAX6","HES1","SOX9")

# markers up in neurons1 - with change in both DA and other neurons
regulate.genes <- c("FZD3","CTNNB1","SOX11","ASCL1","SOX4",
                    "TCF4","CALR","OTX2","PAX6","EFNB2", 
                    "CD24","KCNQ1OT1","CCNG2","HES6")

# top up in neurons 1 
# "CD24"     "KCNQ1OT1" "CCNG2"    "ASCL1"    "HES6" 

# selected up regulated in Neurons 1
regulate.genes <- c("FZD3","ASCL1","SOX4",
                    "TCF4","CALR","PAX6", 
                    "CD24","KCNQ1OT1","CCNG2")

DotPlot(all.neurons, group.by = 'orig.ident', features = regulate.genes,
        split.by = 'Cell_Type2') + RotatedAxis()
# all the expression levels are lower in regular neurons than DA neurons

DotPlot(all.neurons, group.by = 'orig.ident', features = regulate.genes) + RotatedAxis()

# up reg in Neurons2
# many MT gene, also "FTH1","FTL","APOE","SAT1","PTN","GABARAP","PTGDS"

# top up reg genes in Neurons2
#"MALAT1"  "MT-ND2"  "MT-CO1"  "MT-CO2"  "MT-ATP6" "MT-CO3"  "MT-ND3"  "MT-CYB" 

regulate.genes <- c("FTH1","FTL","APOE","SAT1","PTN","GABARAP","PTGDS","SPARCL1",
                    "RPL17","MTRNR2L12","MTRNR2L8","SNHG25","SELENOW",
                    "CRYAB", "PEA15", "ATP1A2", "SELENOK","IGFBP7", "RAB3B")
# strongly altered genes
regulate.genes <- c("FTH1","FTL","PTN","PTGDS","SPARCL1",
                    "SELENOW","CRYAB", "MALAT1", "MT-ND2", "MT-CO1", "MT-CO2",
                    "MT-ATP6", "MT-CO3", "MT-ND3", "MT-CYB")
regulate.genes <- c("MT-ND2", "MT-CO1", "MT-CO2",
                    "MT-ATP6", "MT-CO3", "MT-ND3",
                    "FTH1","FTL",
                    "CRYAB")

# genes up and down 
regulate.genes <- c("FZD3","ASCL1","SOX4",
                    "TCF4","CALR","PAX6", 
                    "CD24","KCNQ1OT1","CCNG2","MT-ND2", "MT-CO1", "MT-CO2",
                    "MT-ATP6", "MT-CO3", "MT-ND3",
                    "FTH1","FTL",
                    "CRYAB")

DotPlot(all.neurons, group.by = 'orig.ident', features = regulate.genes) + RotatedAxis()

pdf(paste(output_path,"DotPlotDEG_N1vsN2.pdf"))
DotPlot(all.neurons, group.by = 'orig.ident', features = regulate.genes) + RotatedAxis()
dev.off()


```









```{r}
BiocManager::install("clusterProfiler")
BiocManager::install("pathview")
BiocManager::install("enrichplot")
library(clusterProfiler)
library(enrichplot)
# we use ggplot2 to add x axis labels (ex: ridgeplot)
library(ggplot2)
```



Try with cluster profiler

```{r}

# need a gene list sorted in decreasing order
# get the log2FC
original_gene_list <- deg.neurons$avg_log2FC
# name the vector - add the gene names
names(original_gene_list) <- rownames(deg.neurons)
gene_list = na.omit(original_gene_list)

gene_list = sort(gene_list, decreasing = TRUE)

# get the gene set enrichment list
library(org.Hs.eg.db)

gse <- gseGO(geneList=gene_list, 
             ont ="BP", 
             keyType = "SYMBOL", 
             minGSSize = 3, 
             maxGSSize = 800, 
             pvalueCutoff = 0.05, 
             verbose = TRUE, 
             OrgDb = org.Hs.eg.db, 
             pAdjustMethod = "none")

require(DOSE)
dotplot(gse, showCategory=20, split=".sign") + facet_grid(.~.sign)

emapplot(gse, showCategory = 10)

```


Predict cell types again to make table 12
Organoids in house data

```{r}

pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"
seu.sc <- readRDS(paste(pathway, "CombinedLabeledMarkers14102022.RDS"))

# predict with AIW
# SNCA and control midbrain organoids 165 days in culture
AST23 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIW120 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


# AST23
seu.r <- AST23
seu.q <- seu.sc

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cluster_labels, k.weight = 50)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")


seu.q$AST23.pred <- ifelse(seu.q$prediction.score.max > 0.4, seu.q$prediction, "none")

DimPlot(seu.q, group.by = 'AST23.pred')

# AIW 60
seu.r <- AIW60
colnames(seu.r@meta.data)

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cluster.ids, k.weight = 50)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")


seu.q$AIW60.pred <- ifelse(seu.q$prediction.score.max > 0.4, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'AIW60.pred')


# AIW 120
seu.r <- AIW120
colnames(seu.r@meta.data)

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$res08names2, k.weight = 50)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")


seu.q$AIW120.pred <- ifelse(seu.q$prediction.score.max > 0.8, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'AIW120.pred')



```


More predictions 
Developing brain: Cortex
                  Forebrain

Adult brain: whole brain with subtypes
Adult brain midbrain and striatum
Adult brain midbrain main cell types
Adult brain DA or Astro subtypes


```{r}


DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")


seu.r <- DAsubtypes
colnames(seu.r@meta.data)

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 20)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")


seu.q$DAsub.pred <- ifelse(seu.q$prediction.score.max > 0.4, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'DAsub.pred')


# astrocytes

astro.ref <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_astro.Rds")
# need to make PCA and UMAP
astro.ref <- NormalizeData(astro.ref)
astro.ref <- FindVariableFeatures(astro.ref, selection.method = "vst", nfeatures = 2000)
astro.ref <- ScaleData(astro.ref)
astro.ref <- RunPCA(astro.ref)
astro.ref <- RunUMAP(astro.ref, reduction = "pca", n.neighbors = 205, dims = 1:25)


seu.r <- astro.ref

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 25)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
seu.q$Astro.sub <- ifelse(seu.q$prediction.score.max > 0.8, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'Astro.sub')


# Midbrain and Striatum 

seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")

# whole brain
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_downsample.RDS")


Idents(seu.r) <- "cell_cluster"

# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster, k.weight = 25)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
seu.q$Brain <- ifelse(seu.q$prediction.score.max > 0.3, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'Brain')


# developing forebrain

seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
colnames(seu.r@meta.data)
Idents(seu.r) <- 'Clusters'

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Clusters, k.weight = 25)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
seu.q$devForebrain <- ifelse(seu.q$prediction.score.max > 0.5, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'devForebrain')

# developing forebrain
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Nowakowski_dev_cortext.RDS")

colnames(seu.r@meta.data)
Idents(seu.r) <- 'WGCNAcluster'

anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
predictions <- TransferData(anchorset = anchors, refdata = seu.r$WGCNAcluster, k.weight = 25)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "prediction")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
seu.q$devcortex <- ifelse(seu.q$prediction.score.max > 0.3, seu.q$prediction, "none")
DimPlot(seu.q, group.by = 'devcortex')

```




Tables of top predictions 

```{r}
#AST23
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$AST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)


#AIW 60

t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)



# AIW 120
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$AIW120.pred))
t.lables$Freq <- as.double(t.lables$Freq)



# DA neuron subtype predictions
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$DAsub.pred))
t.lables$Freq <- as.double(t.lables$Freq)


# Astro subtype
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$Astro.sub))
t.lables$Freq <- as.double(t.lables$Freq)

# Midbrain Bhaduni predictions
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$Midbrain))
t.lables$Freq <- as.double(t.lables$Freq)

# whole brain
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$Brain))
t.lables$Freq <- as.double(t.lables$Freq)

# developing forebrain
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$devForebrain))
t.lables$Freq <- as.double(t.lables$Freq)

# developing cortex
t.lables <- as.data.frame(table(seu.q$Cell_Subtype_Markers,seu.q$devcortex))
t.lables$Freq <- as.double(t.lables$Freq)

top.prediction <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))
top.prediction




```






